Cartographers collect, analyze, and visualize geographic data to create maps, charts, and digital spatial products. Using GIS software, remote sensing imagery, GPS data, and surveying records, they translate complex spatial information into clear, accurate, and visually effective representations. Their work supports everything from navigation apps and urban planning to environmental conservation, military operations, and emergency response.
Modern cartography is highly digital, blending design sensibility with data science, programming, and geospatial analysis. Cartographers work for government agencies, engineering and surveying firms, utilities, tech companies, and environmental organizations. The field rewards strong attention to detail, comfort with GIS tools like ArcGIS and QGIS, and increasingly, skills in Python, SQL, and web mapping, as demand shifts toward interactive and real-time mapping applications.
| Entry level | $48,000 |
| Median | $75,000 |
| Senior | $102,000 |
| Top 10% | $125,000 |
| Job growth | +5% |
| Professionals in the USA | 0.015 million |
| Typical hours/week | 40 hrs |
| Remote work share | 35% |
| Annual job openings | 1,300/yr |
| Demand | 3.7 / 10 (Low) |
AI and machine learning are transforming cartography by automating feature extraction from satellite and aerial imagery, generating base maps, and updating datasets at scale. Routine digitizing and map production tasks are increasingly handled by algorithms, shifting the cartographer's role toward quality control, design, and data interpretation. The field is evolving rather than disappearing, but entry-level production roles are most exposed.
Automation exposure: Automated feature extraction (roads, buildings, land cover) from imagery, auto-generalization of map features, routine map tile generation and styling, label placement, and basic data cleaning and updating of geospatial databases are increasingly automated by AI and GIS software.
The human edge: Cartographers bring design judgment, audience-specific communication, ethical decisions about what to show or omit, handling of politically sensitive boundaries, validation of ambiguous or erroneous AI output, and the ability to translate complex spatial data into clear, persuasive visual stories. Domain expertise and accountability for accuracy remain human responsibilities.
Figures are estimates for exploration — verify current data with BLS.gov.